Utilising Artificial Intelligence (AI) and Machine Learning (ML) algorithms from Google that decoded massive sets of data from NASA’s Kepler Space Telescope, scientists have discovered two exoplanets, including one that has the first known eight-planet system like ours.

Machine learning is an approach to Artificial Intelligence (AI) in which computers “learn.” In this case, computers learned to identify planets by finding in the Kepler data instances where the telescope recorded signals from planets beyond our solar system, known as exoplanets.

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One of the two newly-discovered planets is Kepler-90i — a sizzling hot, rocky planet that orbits its star once every 14.4 days. This marks the discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light-years from Earth – making it the first known eight-planet system outside of our own.

“Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90,” NASA said in a statement on Friday.

“It (Kepler-90i) is 30 per cent larger than Earth, and with a surface temperature of approximately 800 degree F (426.6 degrees Celsius) – not ideal for your next vacation. It also orbits its star every 14 days, meaning you would have a birthday there just about every two weeks,” the researchers said in a Google blog post.

The discovery came after Christopher Shallue and Andrew Vanderburg at Google AI trained a computer to learn how to identify exoplanets in the light readings recorded by Kepler – the minuscule change in brightness captured when a planet passed in front of, or transited, a star.

Inspired by the way neurons connect in the human brain, this artificial “neural network” sifted through Kepler data and found weak transit signals from a previously-missed eighth planet orbiting Kepler-90, in the constellation Draco.

While machine learning has previously been used in searches of the Kepler database, this research demonstrates that neural networks are a promising tool in finding some of the weakest signals of distant worlds.

Shallue, a senior software engineer with Google AI research team, came up with the idea to apply a neural network to the Kepler data.

“In my spare time, I started googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” said Shallue.

First, the researchers trained the neural network to identify transiting exoplanets using a set of 15,000 previously-vetted signals from the Kepler exoplanet catalogue.

In the test set, the neural network correctly identified true planets and false positives 96 per cent of the time.

Then, with the neural network having “learned” to detect the pattern of a transiting exoplanet, the researchers directed their model to search for weaker signals in 670 star systems that already had multiple known planets.

Their assumption was that multiple-planet systems would be the best places to look for more exoplanets.

“We got lots of false positives of planets, but also potentially more real planets,” said Vanderburg.

“It’s like sifting through rocks to find jewels. If you have a finer sieve then you will catch more rocks but you might catch more jewels, as well.”

Kepler-90i wasn’t the only jewel this neural network sifted out. In the Kepler-80 system, they also found a sixth planet.

This one, the Earth-sized Kepler-80g, and four of its neighbouring planets form what is called a resonant chain where planets are locked by their mutual gravity in a rhythmic orbital dance.

The result is an extremely stable system, similar to the seven planets in the TRAPPIST-1 system.

The findings will be published in the forthcoming issue of The Astronomical Journal.

The researchers said they plan to apply their neural network to Kepler’s full set of more than 150,000 stars.